##
rm(list=ls())
gc()


library(data.table)
library(ggplot2)
library(dplyr)
library(stringr)

files = paste0('results/',c("current-results-2012-2016-S-18-34-white.Rdata",
                                        "current-results-2012-2016-S-35-49-white.Rdata",
                                        "current-results-2012-2016-S-50-64-white.Rdata",
                                        "current-results-2012-2016-S-65+-white.Rdata",
                                        
                                        "current-results-2012-2016-H-18-34-white.Rdata",
                                        "current-results-2012-2016-H-35-49-white.Rdata",
                                        "current-results-2012-2016-H-50-64-white.Rdata",
                                        "current-results-2012-2016-H-65+-white.Rdata",
  
  
                                        "current-results-2016-2020-S-18-34-white.Rdata",
                                        "current-results-2016-2020-S-35-49-white.Rdata",
                                        "current-results-2016-2020-S-50-64-white.Rdata",
                                        "current-results-2016-2020-S-65+-white.Rdata",
                                        
                                        "current-results-2016-2020-H-18-34-white.Rdata",
                                        "current-results-2016-2020-H-35-49-white.Rdata",
                                        "current-results-2016-2020-H-50-64-white.Rdata",
                                        "current-results-2016-2020-H-65+-white.Rdata"
                                        ))




results = rbindlist(lapply(files, FUN=function(file){

  
  load(file)
  
  models = ls()[grepl('Model',ls())]
  
  l = rbindlist(lapply(models, FUN=function(x){
   # print(x)
    m = get(x)
    out = as.data.table(m$coefficient)
    out[,Covariate:=rownames(m$coefficient)]
    out[,Model:=x]
    out[,N:=m$N]
    out[,`R-Squared`:=m$r.squared]
    out[,`Adjusted R-Squared`:=m$adj.r.squared]
    out[,Sigma:=m$sigma]
    out[,FStat:=m$fstat]
    if('Cluster s.e.'%in%names(out)){
      out[,SE:=`Cluster s.e.`]
      out[,SE.type:='Cluster']
      out[,`Cluster s.e.`:=NULL]
    } else {
      out[,SE:=`Robust s.e`]
      out[,SE.type:='Robust']
      out[,`Robust s.e`:=NULL]
    }


return(out)

  }))
  
  l[grepl('2008',file),Year1:='2008']
  l[grepl('2008',file),Year2:='2012']
l[grepl('2020',file),Year1:='2016']
l[grepl('2020',file),Year2:='2020'] 
l[!grepl('2008',file)&!grepl('2020',file),Year1:='2012']
l[!grepl('2008',file)&!grepl('2020',file),Year2:='2016']
l[,file:=file]

return(l)
}))


results = results[Covariate %in% c('DemSpExpDiff_nohh','RepSpExpDiff_nohh')]

results[grepl('-H-',file),Housing:='Apartment']
results[grepl('-S-',file),Housing:='Single-family']
results[grepl('18-34',file),Age:='18-34']
results[grepl('35-49',file),Age:='35-49']
results[grepl('50-64',file),Age:='50-64']
results[grepl('65+',file),Age:='65+']

results[grepl('DemSpExp',Model),Exposure.Type:='Effect of\nexposure to\nDemocrats on\nDemocratic\nregistration']
results[grepl('RepSpExp',Model),Exposure.Type:='Effect of\nexposure to\nRepublicans on\nRepublican\nregistration']

results[grepl('Dems',Model),Subset:='Democrats']
results[grepl('Reps',Model),Subset:='Republicans']
results[grepl('Oths',Model),Subset:='Non-partisans']
results[,Years:=paste0(Year1,'-',Year2)]

colors = c(Democrats = "#377EB8", Republicans = "#E41A1C", `Non-partisans` = "purple")



g1 = ggplot(results[Years=='2016-2020'], aes(y = Estimate, x = Age, color = Subset, shape = Housing))+
  geom_point(size = 5, position = position_dodge(width = 0.75))+
  geom_errorbar(aes(ymin = Estimate-qnorm(.975)*SE, ymax = Estimate+qnorm(.975)*SE), size = 1, position = position_dodge(width=0.75), width = 0)+
  geom_hline(yintercept = 0, linetype = 'dashed')+
  coord_flip()+
  theme_bw()+
  scale_color_manual(values = colors)+
  # scale_shape_manual(values = 1:2)+
  ylab('Coefficient on change in partisan exposure')+
  xlab('2016 Age')+
  guides(color = 'none')+
  theme(text = element_text(size = 20, family = 'serif'), legend.title=element_blank(),strip.text.y = element_text(size=12,angle=0),legend.position = 'bottom')+
  facet_grid(Exposure.Type~Subset)

ggsave(plot = g1,filename = 'figures/FigS3b.png',dpi=300, width = 11, height = 5, units = 'in')


g2 = ggplot(results[Years=='2012-2016'], aes(y = Estimate, x = Age, color = Subset, shape = Housing))+
  geom_point(size = 5, position = position_dodge(width = 0.75))+
  geom_errorbar(aes(ymin = Estimate-qnorm(.975)*SE, ymax = Estimate+qnorm(.975)*SE), size = 1, position = position_dodge(width=0.75), width = 0)+
  geom_hline(yintercept = 0, linetype = 'dashed')+
  coord_flip()+
  theme_bw()+
  scale_color_manual(values = colors)+
  #  scale_shape_manual(values = 1:2)+
  ylab('Coefficient on change in partisan exposure')+
  xlab('2012 Age')+
  guides(color = 'none')+
  theme(text = element_text(size = 20, family = 'serif'), legend.title=element_blank(),strip.text.y = element_text(size=12,angle=0),legend.position = 'bottom')+
  facet_grid(Exposure.Type~Subset)

ggsave(plot = g2,filename = 'figures/FigS3a.png',dpi=300, width = 11, height = 5, units = 'in')

